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| 037 | _ | _ | |a FZJ-2020-02652 |
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| 100 | 1 | _ | |a Morales-Gregorio, Aitor |0 P:(DE-Juel1)176593 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 29th Annual Computational Neuroscience Meeting CNS*2020 |g CNS2020 |c Online |d 2020-07-18 - 2020-07-23 |w Online |
| 245 | _ | _ | |a Estimation of the cortical microconnectome from in vivo spiking activity in the macaque monkey |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a The typical range of local connectivity in the cerebral cortex delineates columnar microcircuits, within which the layer- and population-specific connectivities present features that are preserved across species and cortical areas. However, when considered in more detail, the internal connectivity structure, i.e. the microconnectome (MC), of such microcircuits is variable across cortical areas. Furthermore, the parameters describing the MC are largely unknown for most cortical areas.Models constructed based on structural data have been able to recover realistic first-order spike train statistics in early sensory cortical areas [1, 2]. These bottom-up models can be constructed owing to the availability of extensive anatomical and physiological data from early visual and somatosensory areas. However, such measurements are less abundant for higher-order cortices, limiting bottom-up modeling until further biological measurements are published.Here we present an analysis that aims to overcome some of the limitations in currently available anatomical data. We use experimentally measured electrophysiological activity from vision-related and motor areas to constrain the connectivity of cortical microcircuit models and infer area-specific features of the MC. The novel experimental data consist of simultaneous layer-resolved laminar recordings from macaque primary motor (M1) and premotor (PMd) cortices [3]; as well as acute simultaneous recordings of macaque dorsolateral prefrontal cortex (dlPFC) and visual area V4. All data were recorded during resting-state sessions, i.e. while the subjects were not performing any task. Data from the resting state are expected to deliver rich dynamics related to the underlying connectivity structure [4].We explore the parameter space of the MC with an evolutionary algorithm using biologically inspired spiking cortical microcircuit models. During the parameter estimation phase, a set of standardized statistical tests, based on established single-neuron and population statistics [5], are used to score the similarity between the simulated data and experimental recordings. The score is calculated based on the overlap between experimental and simulated data statistics via the Wasserstein distance. Parameter estimates are obtained by maximizing this score, and are then validated against a separate set of statistics, which were not used in the estimation phase. Finally, we assess the similarities and differences of estimated model parameters across areas.Future work will integrate these local visual and motor models into a large-scale visuomotor cortical multi-area model, extending the work in [2, 6].References:1. Potjans TC, Diesmann M. Cereb Cortex 2014, 24(3), 785–8062. Schmidt M, Bakker R et al. Brain Struct Func 2017, 223, 1409–14353. Kilavik BE. SfN 2018. Online4. Dąbrowska P, Voges N et al. On the complexity of resting state spiking activity in monkey motor cortex. In preparation5. Gutzen R, von Papen M et al. Front Neuroinform 2018, 12:906. Schmidt M, Bakker R et al. PLOS CB 2018, 14, e1006359 |
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| 700 | 1 | _ | |a Dabrowska, Paulina |0 P:(DE-Juel1)171408 |b 1 |u fzj |
| 700 | 1 | _ | |a Gutzen, Robin |0 P:(DE-Juel1)171572 |b 2 |u fzj |
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